1. Query and Document Operations - 1 Terms and Query Operations Hsin-Hsi Chen

2. Query and Document Operations - 2 Lexical Analysis and Stoplists

3. Query and Document Operations - 3 Lexical Analysis for Automatic Indexing Lexical Analysis Convert an input stream of characters into stream words or token. What is a word or a token? Tokens consist of letters. –digits: Most numbers are not good index terms. counterexamples: case numbers in a legal database, “B6” and “B12” in vitamin database. –Hyphens break hyphenated words: state-of-the-art, state of the art keep hyphenated words as a token: “Jean-Claude”, “F-16”

4. Query and Document Operations - 4 Lexical Analysis for Automatic Indexing (Continued) –other punctuation: often used as parts of terms, e.g., OS/2 –Case: usually not significant in index terms Issues: recall and precision –breaking up hyphenated terms increase recall but decrease precision –preserving case distinctions enhance precision but decrease recall –commercial information systems usually take recall enhancing approach

5. Query and Document Operations - 5 Lexical Analysis for Query Processing Tasks –depend on the design strategies of the lexical analyzer for automatic indexing (search terms must match index terms) –distinguish operators like Boolean operators, stemming or truncating operators, and weighting functions –distinguish grouping indicators like parentheses and brackets

6. Query and Document Operations - 6 stoplist (negative dictionary) Avoid retrieving almost very item in a database regardless of its relevance. Example (derived from Brown corpus): 425 words a, about, above, across, after, again, against, all, almost, alone, along, already, also, although, always, among, an, and, another, any, anybody, anyone, anything, anywhere, are, area, areas, around, as, ask, asked, asking, asks, at, away, b, back, backed, backing, backs, be, because, became,...

7. Query and Document Operations - 7 Implementing Stoplists approaches –examine lexical analyzer output and remove any stopwords –remove stopwords as part of lexical analysis

8. Query and Document Operations - 8 Stemming Algorithms

9. Query and Document Operations - 9 Stemmers programs that relate morphologically similar indexing and search terms stem at indexing time –advantage: efficiency and index file compression –disadvantage: information about the full terms is lost example (CATALOG system), stem at search time Look for: system users Search Term: users TermOccurrences 1. user15 2. users1 3. used3 4. using2 Which terms (0=none, CR=all):

10. Query and Document Operations - 10 Conflation Methods manual automatic (stemmers) –affix removal longest match vs. simple removal –successor variety –table lookup –n-gram evaluation –correctness –retrieval effectiveness –compression performance

11. Query and Document Operations - 11 Successor Variety Definition (successor variety of a string) the number of different characters that follow it in words in some body of text Example a body of text: able, axle, accident, ape, about successor variety of apple 1st: 4 (b, x, c, p) 2nd: (e)

12. Query and Document Operations - 12 Successor Variety (Continued) Idea The successor variety of substrings of a term will decrease as more characters are added until a segment boundary is reached, i.e., the successor variety will sharply increase. Example Test word: READABLE Corpus:ABLE, BEATABLE, FIXABLE, READ, READABLE, READING, RED, ROPE, RIPE PrefixSuccessor VarietyLetters R3E, O, I RE2A, D REA1D READ3A, I, S READA1B READAB1L READABL1E READABLE1blank

13. Query and Document Operations - 13 The successor variety stemming process Determine the successor variety for a word. Use this information to segment the word. –cutoff method a boundary is identified whenever the cutoff value is reached –peak and plateau method a character whose successor variety exceeds that of the character immediately preceding it and the character immediately following it –complete word method a segment is a complete word –entropy method Select one of the segments as the stem.

14. Query and Document Operations - 14 n-gram stemmers diagram a pair of consecutive letters shared diagram method association measures are calculated between pairs of terms where A: the number of unique diagrams in the first word, B: the number of unique diagrams in the second, C: the number of unique diagrams shared by A and B.

15. Query and Document Operations - 15 n-gram stemmers (Continued) Example statistics => st ta at ti is st ti ic cs unique diagrams => at cs ic is st ta ti statistical => st ta at ti is st ti ic ca al unique diagrams => al at ca ic is st ta ti

16. Query and Document Operations - 16 n-gram stemmers (Continued) similarity matrix determine the semantic measures for all pairs of terms in the database word 1 word 2 word 3...word n-1 word 1 wrod 2 S 21 word 3 S 31 S 32.. Word n S n1 S n2 S n3 …S n(n-1) terms are clustered using a single link clustering method

17. Query and Document Operations - 17 Affix Removal Stemmers procedure Remove suffixes and/or prefixes from terms leaving a stem, and transform the resultant stem. example: plural forms If a word ends in “ies” but not “eies” or “aies” then “ies” --> “y” If a word ends in “es” but not “aes”, “ees”, or “oes” then “es” --> “e” If a word ends in “s”, but not “us” or “ss” then “s” --> NULL ambiguity

18. Query and Document Operations - 18 Affix Removal Stemmers (Continued) longest match stemmer remove the longest possible string of characters from a word according to a set of rules –recoding: AxC--> AyC, e.g., ki --> ky –partial matching: only n initial characters of stems are used in comparing different versions Lovins, Slaton, Dawson, Porter, … Students can refer to the rules listed in the text book.

19. Query and Document Operations - 19 Thesaurus Constructions

20. Query and Document Operations - 20 Thesaurus Construction IR thesaurus a list of terms (words or phrases) along with relationships among them physics, EE, electronics, computer and control INSPEC thesaurus (1979) cesium ( 銫， Cs) USE caesium (the preferred form) computer-aided instruction see also education (cross-referenced terms) UF teaching machines (a set of alternatives) BT educational computing (broader terms, cf. NT) TT computer applications (root node/top term) RT education (related terms) teaching CC C7810C (subject area) FC C7810Cf (subject area)

21. Query and Document Operations - 21 Usage Indexing Select the most appropriate thesaurus entries for representing the document. Searching Design the most appropriate search strategy. –If the search does not retrieve enough documents, the thesaurus can be used to expand the query. –If the search retrieves too many items, the thesaurus can suggest more specific search vocabulary.

22. Query and Document Operations - 22 Features of Thesauri Coordination Level –pre-coordination: phrases phrases are available for indexing and retrieval advantage: reducing ambiguity in indexing and searching disadvantage: searcher has to be know the phrase formulation rules –post-coordination: words phrases are constructed while searching advantage: users do not worry about the exact word ordering disadvantage: the search precision may fall, e.g., library school vs. school library –immediate level: phrases and single words the higher the level of coordination, the greater the precision of the vocabulary but the larger the vocabulary size

23. Query and Document Operations - 23 Features of Thesauri (Continued) Term Relationships –Aitchison and Gilchrist (1972) equivalence relationships –synonymy: trade names, popular and local usage, superseded terms –quasi-synonymy, e.g., harshness and tenderness hierarchical relationships, e.g., genus-species nonhierarchical relationships, e.g., thing-part, thing-attribute

24. Query and Document Operations - 24 Features of Thesauri (Continued) –Wang, Vandendorpe, and Evens (1985) parts-wholes, e.g., set-element, count-mass collocation relations: words that frequently co-occur in the same phrase or sentence paradigmatic relations: words that have the same semantic core, e.g., “moon” and “lunar” taxonomy and synonymy antonymy relations

25. Query and Document Operations - 25 Features of Thesauri (Continued) Number of entries for each term –homographs: words with multiple meanings –each homograph entry is associated with its own set of relations –problem: how to select between alternative meanings Specificity of vocabulary –the precision associated with the component terms –a highly specific vocabulary promotes precision in retrieval

26. Query and Document Operations - 26 Features of Thesauri (Continued) Control on term frequency of class members –for statistical thesaurus construction methods –terms included in the same thesaurus class have roughly equal frequencies –the total frequency in each class should also be roughly similar Normalization of vocabulary –terms should be in noun form –noun phrases should avoid prepositions unless they are commonly known –a limited number of adjectives should be used –...

27. Query and Document Operations - 27 Thesaurus Construction manual thesaurus construction –define the boundaries of the subject area –collect the terms for each subarea sources: indexes, encyclopedias, handbooks, textbooks, journal titles and abstracts, catalogues,... –organize the terms and their relationship into structures –review (and refine) the entire thesaurus for consistency automatic thesaurus construction –from a collection document items –by merging existing thesaurus

28. Query and Document Operations - 28 Thesaurus Construction from Texts 1. Construction of vocabulary normalization and selection of terms phrase construction depending on the coordination level desired 2. Similarity computations between terms identify the significant statistical associations between terms 3. Organization of vocabulary organize the selected vocabulary into a hierarchy on the basis of the associations computed in step 2.

29. Query and Document Operations - 29 Construction of Vocabulary Objective identify the most informative terms (words and phrases) Procedure (1) Identify an appropriate document collection. The document collection should be sizable and representative of the subject area. (2) Determine the required specificity for the thesaurus. (3) Normalize the vocabulary terms. (a) Eliminate very trivial words such as prepositions and conjunctions. (b) Stem the vocabulary. (4) Select the most interesting stems, and create interesting phrases for a higher coordination level.

30. Query and Document Operations - 30 Stem evaluation and selection selection by frequency of occurrence –each term may belong to category of high, medium or low frequency –terms in the mid-frequency range are the best for indexing and searching

31. Query and Document Operations - 31 Stem evaluation and selection (Continued) selection by discrimination value (DV) –the more discriminating a term, the higher its value as an index term –procedure compute the average inter-document similarity in the collection Remove the term K from the indexing vocabulary, and recompute the average similarity DV(K)= (average similarity without K)-(average similarity with k) The DV for good discriminators is positive.

32. Query and Document Operations - 32 Phrase Construction Salton and McGill procedure 1. Compute pairwise co-occurrence for high-frequency words. 2. If this co-occurrence is lower than a threshold, then do not consider the pair any further. 3. For pairs that qualify, compute the cohesion value. COHESION(t i, t j )= co-occurrence-frequency/(sqrt(frequency(t i )*frequency(t j ))) COHESION(t i, t j )=size-factor* co-occurrence-frequency/(frequency(t i )*frequency(t j )) where size-factor is the size of thesaurus vocabulary 4. If cohesion is above a second threshold, retain the phrase

33. Query and Document Operations - 33 Phrase Construction (Continued) Choueka Procedure 1. Select the range of length allowed for each collocational expression. E.g., 2-6 wsords 2. Build a list of all potential expressions from the collection with the prescribed length that have a minimum frequency. 3. Delete sequences that begin or end with a trivial word (e.g., prepositions, pronouns, articles, conjunctions, etc.) 4. Delete expressions that contain high-frequency nontrivial words. 5. Given an expression, evaluate any potential sub-expressions for relevance. Discard any that are not sufficiently relevant. 6. Try to merge smaller expressions into larger and more meaningful ones.

34. Query and Document Operations - 34 Similarity Computation Cosine compute the number of documents associated with both terms divided by the square root of the product of the number of documents associated with the first term and the number of documents associated with the second term. Dice compute the number of documents associated with both terms divided by the sum of the number of documents associated with one term and the number associated with the other.

35. Query and Document Operations - 35 Vocabulary Organization Assumptions: (1) high-frequency words have broad meaning, while low- frequency words have narrow meaning. (2) if the density functions of two terms have the same shape, then the two words have similar meaning. 1. Identify a set of frequency ranges. 2. Group the vocabulary terms into different classes based on their frequencies and the ranges selected in step 1. 3. The highest frequency class is assigned level 0, the next, level 1, and so on. 4. Parent-child links are determined between adjacent levels as follows. For each term t in level i, compute similarity between t and every term in level i-1. Term t becomes the child of the most similar term in level i-1. If more than one term in level i-1 qualifies for this, then each becomes a parent of t. In other words, a term is allowed to have multiple parents. 5. After all terms in level i have been linked to level i-1 terms, check level i-1terms and identify those that have no children. Propagate such terms to level i by creating an identical “dummy” term as its child. 6. Perform steps 4 and 5 for each level starting with level.

36. Query and Document Operations - 36 Merging Existing Thesauri simple merge link hierarchies wherever they have terms in common complex merge –link terms from different hierarchies if they are similar enough. –similarity is a function of the number of parent and child terms in common

37. Query and Document Operations - 37 Relevance Feedback and Other Query Modification Techniques

38. Query and Document Operations - 38 Paraphrase Problem in IR Users often input queries containing terms that do not match the terms used to index the majority of the relevant documents. relevance feedback and query modification –reweighting of the query terms based on the distribution of these terms in the relevant and nonrelevant documents retrieved in response to those queries –changing the actual terms in the query

39. Query and Document Operations - 39 Early Research Rocchio model –Q 0 : the vector for the initial query –R i : the vector for relevant document i –S i : the vecotr for nonrelevant document I –n 1 : the number of relevant documents –n 2 : the number of nonrelevant documents

40. Query and Document Operations - 40 Early Research (Continued) –Rocchio constraint only allowing terms to be in Q 1 if they either were in Q 0 or occurred in at least half the relevant documents and in more relevant than nonrelevant documents –Ide constraints Ricchio formula minus the normalization for the number of relevant and nonrelevant documents allow feedback from relevant documents allow limited negative feedback from only the highest-ranked nonrelevant document the relevant only strategies worked best for some queries, and other queries did better using negative feedback in addition

41. Query and Document Operations - 41 Evaluation of relevance feedback Standard evaluation (i.e., recall-precision) method is not suitable, because the relevant documents used to reweight the query terms moving to higher ranks. The residual collection method –the evaluation of the results compares only the residual collections, i.e., the initial run is remade minus the documents previously shown the user and this is compared with the feedback run minus the same documents

42. Query and Document Operations - 42 Research in term reweighting without query expansion Distribution of query terms in relevant and nonrelevant documents –Wij: the term weight for term i in query j –r: the number of relevant documents for query j having term i –R: the total number of relevant documents for query j –n: the number of documents in the collection having term i –N: the number of documents in the collection N n N-n relevant R documents rR-r n-r N-n-R+r

43. Query and Document Operations - 43 Research in term reweighting without query expansion (Continued) Sparck Jones –a user sees only a few relevant documents in the initial set of retrieved documents –those documents are the only ones available to the weighting scheme –add a constant to the above formula to handle situations in which query terms appeared none of the retrieved documents

44. Query and Document Operations - 44 Croft’s method Initial search Feedback W ijk : the term weight for term i in query j and document k IDF i : the IDF weight for term i in the entire collection p ij : the probability that term i is assigned within the set of relevant documents for query j q ij : the probability that term i is assigned within the set of nonrelevant documents for query j r: the number of relevant documents for query j having term i R: the total number of relevant documents for query j n: the number of documents in the collection having term i N: the number of documents in the collection where r > 0 where r = 0

45. Query and Document Operations - 45 Query Expansion without Term Reweighting Query expansion can be done using a thesaurus that adds synonyms, broader terms, and other appropriate words Query expansion can also be done using list of terms from relevant documents –significant differences between the ranking methods –too many terms from sorted list decreased performance –using simulated perfect user selection produces improvement over methods without user selection

46. Query and Document Operations - 46 Query Expansion with Term Reweighting Salton and Buckley methods –Ide Regular –Ide dec-hi –Standard Rocchio –where Q 0 : the vector for the initial query R i : the vector for relevant document i S i : the vector for nonrelevant document i n 1 : the number of relevant documents n 2 : the number of nonrelevant documents

47. Query and Document Operations - 47 Relevance Feedback Methodologies Issues –the type of retrieval system being used for initial searching Boolean-based systems systems based on using a vector space model systems based on ranking using either an ad hoc combination of term-weighting or using the probabilistic indexing methods –the type of data that is being used the length of the documents (short or not short) the type of indexing (controlled or full text)

48. Query and Document Operations - 48 Feedback in Boolean retrieval systems options –front-end construction or specially modified Boolean systems require major modifications to the entire systems and can be difficult to tune offer the greatest improvement in performance –produce ranked lists of terms for user selection improve the query by suggesting alternative terms show the user the term distribution in the retrieved set of documents

49. Query and Document Operations - 49 Feedback in retrieval systems based on the vector space model Combine term reweighting and query expansion Ide de-chi method ( ) is the best general purpose feedback technique the use of normalized vector space document weighting is highly recommended add only a limited number of terms (mainly to improve response time)

50. Query and Document Operations - 50 Feedback in retrieval systems based on the vector space model (Continued) –The terms to be added to the query are automatically pulled from a sorted list of new terms taken from relevant documents –These terms are sorted by their total frequency within all retrieved relevant documents –The number of terms added is the average number of terms in the retrieved relevant documents –alternative: substitute user selection from the top of the list rather than adding a fixed number of terms

51. Query and Document Operations - 51 Feedback in retrieval systems based on other types of statistical ranking The term-weighting and query expansion can be viewed as two separate components of feedback, with no specific relationship

52. Query and Document Operations - 52 Term-weighting scheme global importance –the reweighting schemes only affect the weighting based on the importance of a term within a given document local importance –term-weighting based on term importance within a given document Robertson-Jones weighting formula

53. Query and Document Operations - 53 Initial search Feedback where r > 0 where r = 0 freq ik :the frequency of term i in document k maxfreq k : the maximum frequency in document k C=0 for automatically indexed collections or for feedback searching (allow IDF or the relevance weighting to be the dominant factor) C>0 for manually indexed collections (allow the mere existence of a term within a document to carry more weight) K=0.3 for initial search of regular length documents (documents having many multiple occurrences of a term) K=0.5 for feedback searches K=1 for short documents: the within-document frequency is removed (the within-document frequency plays a minimum role)

54. Query and Document Operations - 54 Query Expansion Query expansion by related terms Query expansion by terms from relevant documents

55. Query and Document Operations - 55 Ranking Algorithms Hsin-Hsi Chen

56. Query and Document Operations - 56 Ranking Models Vector Space Model td ij : the i th term in the vector for document j tq ik : the i th term in the vector for query k n: the number of unique terms in the data set

57. Query and Document Operations - 57 Ranking Models (Continued) Probabilistic Model –terms that appear in previously retrieved documents for a given query should be given a higher weight than if they had not appeared in those relevant documents

58. Query and Document Operations - 58 Document Relevance Document Indexing + - +- r R-r R n-r N-n-R+r N-R n N-n N N: the number of documents in the collection R: the number of relevant documents for query q n: the number of documents having term t r: the number of relevant documents having term t relative distribution of terms in the relevant and nonrelevant documents

59. Query and Document Operations - 59 (1) Croft and Harper Q: the number of matching terms between document j and query k C: a constant for tuning the similarity function n i : the number of documents having term i in the data set N: the number of documents in the data set (2) Croft